The training, validation, and test data of the WikiHop dataset are created using distant supervision, but the validation and test data are manually verified. WikiHop test data is blind and not released. So we use their validation data to create the test data for our task and use their training data for our training and validation purposes. We include the statistics of our multi-hop relation extraction dataset in Table REF . We include the statistics on the number of common entities present in the two documents of a chain in Table REF . We split the training data randomly, with 90% for training and 10% for validation. From Table REF , we see that the dataset contains a much higher number of None tuples than the positive tuples. So we randomly select None tuples so that the number of None tuples is the same as the number of positive tuples for training and validation. For evaluation, we consider the entire test dataset. From Table REF , we see that our MHRED dataset contains more relations than any other distantly supervised relation extraction datasets such as the New York Times [1]}, [2]} or DocRED [3]}.
<TABLE><TABLE><TABLE>